from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
measurements = ['iteration_throughput', 'latency', 'mean_duration_sklearn', 'mean_duration_sklearnex', 'speedup', 'std_duration_sklearn', 'std_duration_sklearnex', 'std_speedup']
def get_position(string):
if "mean_duration" in string:
return 3
elif "std_duration" in string:
return 2
elif "score" in string:
return 1
elif "speedup" in string:
return 0
else:
return -1
sorted(measurements, key=get_position, reverse=True)
['mean_duration_sklearn', 'mean_duration_sklearnex', 'std_duration_sklearn', 'std_duration_sklearnex', 'speedup', 'std_speedup', 'iteration_throughput', 'latency']
reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 2.345982 | 0.197174 | NaN | 0.000341 | 0.002346 | brute | -1 | 1 | 0.663 | 0.471125 | 0.006206 | 0.687 | 4.979534 | 4.979966 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 3.136109 | 0.100838 | NaN | 0.000255 | 0.003136 | brute | -1 | 5 | 0.757 | 0.472683 | 0.006361 | 0.742 | 6.634696 | 6.635296 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.443875 | 0.027407 | NaN | 0.000327 | 0.002444 | brute | 1 | 100 | 0.882 | 0.536968 | 0.007184 | 0.875 | 4.551247 | 4.551654 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.025788 | 0.002638 | NaN | 0.000031 | 0.025788 | brute | 1 | 100 | 1.000 | 0.011406 | 0.000669 | 0.000 | 2.260944 | 2.264826 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 3.139071 | 0.062996 | NaN | 0.000255 | 0.003139 | brute | -1 | 100 | 0.882 | 0.529779 | 0.010274 | 0.875 | 5.925245 | 5.926359 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.028726 | 0.002505 | NaN | 0.000028 | 0.028726 | brute | -1 | 100 | 1.000 | 0.011955 | 0.001017 | 0.000 | 2.402805 | 2.411487 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.442894 | 0.022442 | NaN | 0.000327 | 0.002443 | brute | 1 | 5 | 0.757 | 0.466291 | 0.009039 | 0.742 | 5.238993 | 5.239977 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.621578 | 0.010942 | NaN | 0.000493 | 0.001622 | brute | 1 | 1 | 0.663 | 0.470931 | 0.013045 | 0.687 | 3.443344 | 3.444664 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.851052 | 0.028637 | NaN | 0.000009 | 0.001851 | brute | -1 | 1 | 0.896 | 0.105019 | 0.007501 | 0.967 | 17.625800 | 17.670700 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.809051 | 0.070351 | NaN | 0.000006 | 0.002809 | brute | -1 | 5 | 0.922 | 0.102533 | 0.002546 | 0.974 | 27.396503 | 27.404945 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 2.212984 | 0.031356 | NaN | 0.000007 | 0.002213 | brute | 1 | 100 | 0.929 | 0.157741 | 0.002225 | 0.975 | 14.029200 | 14.030595 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.779325 | 0.060005 | NaN | 0.000006 | 0.002779 | brute | -1 | 100 | 0.929 | 0.158738 | 0.003885 | 0.975 | 17.508870 | 17.514113 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 2.256987 | 0.044696 | NaN | 0.000007 | 0.002257 | brute | 1 | 5 | 0.922 | 0.102296 | 0.001294 | 0.974 | 22.063359 | 22.065123 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.317206 | 0.014492 | NaN | 0.000012 | 0.001317 | brute | 1 | 1 | 0.896 | 0.101021 | 0.002322 | 0.967 | 13.038872 | 13.042315 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.744 | 0.0 | -1 | 1 | 0.059 | 0.005 | 0.236 | 0.237 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.013 | 0.001 | 5.935 | 0.0 | -1 | 5 | 0.062 | 0.005 | 0.219 | 0.220 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.000 | 5.890 | 0.0 | 1 | 100 | 0.059 | 0.001 | 0.229 | 0.229 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.652 | 0.0 | -1 | 100 | 0.056 | 0.002 | 0.252 | 0.252 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.914 | 0.0 | 1 | 5 | 0.059 | 0.001 | 0.229 | 0.229 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.015 | 0.001 | 5.358 | 0.0 | 1 | 1 | 0.057 | 0.002 | 0.261 | 0.261 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.314 | 0.0 | -1 | 1 | 0.009 | 0.001 | 0.585 | 0.588 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.326 | 0.0 | -1 | 5 | 0.009 | 0.001 | 0.528 | 0.534 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.001 | 0.294 | 0.0 | 1 | 100 | 0.009 | 0.001 | 0.612 | 0.616 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.343 | 0.0 | -1 | 100 | 0.009 | 0.001 | 0.496 | 0.497 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.320 | 0.0 | 1 | 5 | 0.009 | 0.001 | 0.566 | 0.567 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.337 | 0.0 | 1 | 1 | 0.009 | 0.001 | 0.521 | 0.523 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.346 | 0.197 | 0.0 | 0.002 | -1 | 1 | 0.471 | 0.006 | 4.980 | 4.980 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.028 | 0.004 | 0.0 | 0.028 | -1 | 1 | 0.011 | 0.001 | 2.402 | 2.405 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 3.136 | 0.101 | 0.0 | 0.003 | -1 | 5 | 0.473 | 0.006 | 6.635 | 6.635 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.027 | 0.003 | 0.0 | 0.027 | -1 | 5 | 0.012 | 0.001 | 2.311 | 2.316 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.444 | 0.027 | 0.0 | 0.002 | 1 | 100 | 0.537 | 0.007 | 4.551 | 4.552 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.026 | 0.003 | 0.0 | 0.026 | 1 | 100 | 0.011 | 0.001 | 2.261 | 2.265 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 3.139 | 0.063 | 0.0 | 0.003 | -1 | 100 | 0.530 | 0.010 | 5.925 | 5.926 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.029 | 0.003 | 0.0 | 0.029 | -1 | 100 | 0.012 | 0.001 | 2.403 | 2.411 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.443 | 0.022 | 0.0 | 0.002 | 1 | 5 | 0.466 | 0.009 | 5.239 | 5.240 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.027 | 0.002 | 0.0 | 0.027 | 1 | 5 | 0.011 | 0.001 | 2.326 | 2.332 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.622 | 0.011 | 0.0 | 0.002 | 1 | 1 | 0.471 | 0.013 | 3.443 | 3.445 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.001 | 0.0 | 0.024 | 1 | 1 | 0.012 | 0.001 | 2.098 | 2.102 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.851 | 0.029 | 0.0 | 0.002 | -1 | 1 | 0.105 | 0.008 | 17.626 | 17.671 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.0 | 0.006 | -1 | 1 | 0.001 | 0.000 | 6.848 | 7.012 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.809 | 0.070 | 0.0 | 0.003 | -1 | 5 | 0.103 | 0.003 | 27.397 | 27.405 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.007 | 0.002 | 0.0 | 0.007 | -1 | 5 | 0.001 | 0.000 | 10.120 | 10.229 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.213 | 0.031 | 0.0 | 0.002 | 1 | 100 | 0.158 | 0.002 | 14.029 | 14.031 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.0 | 0.003 | 1 | 100 | 0.001 | 0.000 | 4.181 | 4.227 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.779 | 0.060 | 0.0 | 0.003 | -1 | 100 | 0.159 | 0.004 | 17.509 | 17.514 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.0 | 0.006 | -1 | 100 | 0.001 | 0.000 | 8.151 | 8.185 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.257 | 0.045 | 0.0 | 0.002 | 1 | 5 | 0.102 | 0.001 | 22.063 | 22.065 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.0 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.736 | 4.777 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.317 | 0.014 | 0.0 | 0.001 | 1 | 1 | 0.101 | 0.002 | 13.039 | 13.042 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.0 | 0.002 | 1 | 1 | 0.001 | 0.000 | 3.311 | 3.340 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.920315 | 1.186926 | NaN | 0.000087 | 0.000920 | kd_tree | -1 | 1 | 0.929 | 0.128069 | 0.003834 | 0.910 | 7.186097 | 7.189316 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.125627 | 0.559873 | NaN | 0.000071 | 0.001126 | kd_tree | -1 | 5 | 0.946 | 0.231097 | 0.007563 | 0.941 | 4.870805 | 4.873413 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 6.058615 | 0.722357 | NaN | 0.000013 | 0.006059 | kd_tree | 1 | 100 | 0.951 | 0.694570 | 0.012779 | 0.940 | 8.722829 | 8.724305 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.511473 | 0.341730 | NaN | 0.000023 | 0.003511 | kd_tree | -1 | 100 | 0.951 | 0.719420 | 0.009270 | 0.940 | 4.880975 | 4.881380 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.879582 | 0.409067 | NaN | 0.000043 | 0.001880 | kd_tree | 1 | 5 | 0.946 | 0.244425 | 0.008042 | 0.941 | 7.689807 | 7.693969 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 1.018047 | 0.382193 | NaN | 0.000079 | 0.001018 | kd_tree | 1 | 1 | 0.929 | 0.130902 | 0.003550 | 0.910 | 7.777167 | 7.780026 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.038381 | 0.016261 | NaN | 0.000417 | 0.000038 | kd_tree | -1 | 1 | 0.891 | 0.000759 | 0.000260 | 0.879 | 50.537893 | 53.421117 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.034384 | 0.001957 | NaN | 0.000465 | 0.000034 | kd_tree | -1 | 5 | 0.911 | 0.000931 | 0.000042 | 0.905 | 36.929226 | 36.966619 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.050384 | 0.014551 | NaN | 0.000318 | 0.000050 | kd_tree | 1 | 100 | 0.894 | 0.005895 | 0.000193 | 0.917 | 8.547459 | 8.552046 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.054541 | 0.011573 | NaN | 0.000293 | 0.000055 | kd_tree | -1 | 100 | 0.894 | 0.007169 | 0.002378 | 0.917 | 7.607410 | 8.014863 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.032208 | 0.001287 | NaN | 0.000497 | 0.000032 | kd_tree | 1 | 5 | 0.911 | 0.001116 | 0.000486 | 0.905 | 28.854770 | 31.473653 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.031884 | 0.003153 | NaN | 0.000502 | 0.000032 | kd_tree | 1 | 1 | 0.891 | 0.000621 | 0.000016 | 0.879 | 51.323294 | 51.340629 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.844 | 0.034 | 0.028 | 0.0 | -1 | 1 | 0.863 | 0.075 | 3.295 | 3.307 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.907 | 0.115 | 0.020 | 0.0 | -1 | 5 | 0.833 | 0.022 | 4.691 | 4.692 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.972 | 0.119 | 0.020 | 0.0 | 1 | 100 | 0.791 | 0.016 | 5.020 | 5.021 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.920 | 0.089 | 0.020 | 0.0 | -1 | 100 | 0.860 | 0.022 | 4.559 | 4.560 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 4.037 | 0.095 | 0.020 | 0.0 | 1 | 5 | 0.826 | 0.019 | 4.888 | 4.889 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.973 | 0.100 | 0.020 | 0.0 | 1 | 1 | 0.858 | 0.021 | 4.630 | 4.631 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.016 | 0.0 | -1 | 1 | 0.005 | 0.004 | 0.209 | 0.270 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.021 | 0.0 | -1 | 5 | 0.003 | 0.002 | 0.263 | 0.334 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.024 | 0.0 | 1 | 100 | 0.002 | 0.001 | 0.351 | 0.447 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.023 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.615 | 0.622 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.021 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.665 | 0.671 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.021 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.682 | 0.685 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.920 | 1.187 | 0.000 | 0.001 | -1 | 1 | 0.128 | 0.004 | 7.186 | 7.189 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.999 | 10.244 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.126 | 0.560 | 0.000 | 0.001 | -1 | 5 | 0.231 | 0.008 | 4.871 | 4.873 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.000 | 0.000 | 0.004 | -1 | 5 | 0.000 | 0.000 | 8.379 | 8.609 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 6.059 | 0.722 | 0.000 | 0.006 | 1 | 100 | 0.695 | 0.013 | 8.723 | 8.724 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.002 | 0.000 | 0.004 | 1 | 100 | 0.001 | 0.000 | 3.853 | 3.984 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.511 | 0.342 | 0.000 | 0.004 | -1 | 100 | 0.719 | 0.009 | 4.881 | 4.881 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.006 | 0.001 | 0.000 | 0.006 | -1 | 100 | 0.001 | 0.000 | 6.081 | 6.218 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.880 | 0.409 | 0.000 | 0.002 | 1 | 5 | 0.244 | 0.008 | 7.690 | 7.694 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 0.001 | 0.000 | 3.317 | 3.442 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.018 | 0.382 | 0.000 | 0.001 | 1 | 1 | 0.131 | 0.004 | 7.777 | 7.780 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.263 | 3.709 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.038 | 0.016 | 0.000 | 0.000 | -1 | 1 | 0.001 | 0.000 | 50.538 | 53.421 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.004 | 0.002 | 0.000 | 0.004 | -1 | 1 | 0.000 | 0.000 | 30.591 | 30.872 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.034 | 0.002 | 0.000 | 0.000 | -1 | 5 | 0.001 | 0.000 | 36.929 | 36.967 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 16.705 | 17.134 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.050 | 0.015 | 0.000 | 0.000 | 1 | 100 | 0.006 | 0.000 | 8.547 | 8.552 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 4.702 | 4.786 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.055 | 0.012 | 0.000 | 0.000 | -1 | 100 | 0.007 | 0.002 | 7.607 | 8.015 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 100 | 0.000 | 0.000 | 14.842 | 15.994 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.032 | 0.001 | 0.000 | 0.000 | 1 | 5 | 0.001 | 0.000 | 28.855 | 31.474 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.887 | 5.055 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.032 | 0.003 | 0.001 | 0.000 | 1 | 1 | 0.001 | 0.000 | 51.323 | 51.341 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 5.482 | 5.664 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.650 | 0.089 | 30 | 0.025 | 0.0 | random | 0.333 | 0.012 | 1.953 | 1.955 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.713 | 0.024 | 30 | 0.022 | 0.0 | k-means++ | 0.375 | 0.008 | 1.899 | 1.900 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 8.114 | 0.229 | 30 | 0.099 | 0.0 | random | 4.441 | 0.139 | 1.827 | 1.828 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 8.363 | 0.025 | 30 | 0.096 | 0.0 | k-means++ | 4.615 | 0.054 | 1.812 | 1.812 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.007 | 0.000 | random | 0.0 | 0.0 | 6.394 | 9.564 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 11.848 | 11.931 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.007 | 0.000 | k-means++ | 0.0 | 0.0 | 9.498 | 10.021 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 10.817 | 11.104 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.367 | 0.000 | random | 0.0 | 0.0 | 7.078 | 7.243 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 9.616 | 9.716 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.001 | 30 | 0.332 | 0.000 | k-means++ | 0.0 | 0.0 | 7.074 | 7.302 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 10.904 | 11.634 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.002383 | 0.000212 | 20 | 0.006714 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000926 | 0.000754 | -0.000965 | 2.572960 | 3.317346 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.002230 | 0.000140 | 20 | 0.007174 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000786 | 0.000131 | -0.000750 | 2.837247 | 2.876539 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.003614 | 0.000142 | 20 | 0.221346 | 0.000004 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.001651 | 0.000094 | 0.293767 | 2.188862 | 2.192424 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.003929 | 0.000348 | 20 | 0.203638 | 0.000004 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001654 | 0.000075 | 0.256968 | 2.374975 | 2.377420 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.113 | 0.003 | 20 | 0.001 | 0.0 | random | 0.059 | 0.004 | 1.901 | 1.905 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.337 | 0.010 | 20 | 0.000 | 0.0 | k-means++ | 0.154 | 0.010 | 2.187 | 2.191 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.372 | 0.005 | 20 | 0.022 | 0.0 | random | 0.294 | 0.004 | 1.264 | 1.264 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 1.245 | 0.021 | 20 | 0.006 | 0.0 | k-means++ | 0.672 | 0.009 | 1.852 | 1.852 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.000 | 20 | 0.007 | 0.000 | random | 0.001 | 0.001 | 2.573 | 3.317 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | random | 0.000 | 0.000 | 10.489 | 10.620 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.000 | 20 | 0.007 | 0.000 | k-means++ | 0.001 | 0.000 | 2.837 | 2.877 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.001 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.000 | 11.001 | 11.929 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.004 | 0.000 | 20 | 0.221 | 0.000 | random | 0.002 | 0.000 | 2.189 | 2.192 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | random | 0.000 | 0.000 | 9.983 | 10.101 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.004 | 0.000 | 20 | 0.204 | 0.000 | k-means++ | 0.002 | 0.000 | 2.375 | 2.377 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.000 | 9.805 | 9.991 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000578 | 0.000703 | [20] | 1.384545 | 5.778073e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000938 | 0.001596 | 0.55 | 0.616274 | 1.216650 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.002624 | 0.000241 | [26] | 3.048718 | 2.624053e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.004787 | 0.001739 | 0.28 | 0.548167 | 0.583234 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 18.399 | 0.242 | [20] | 0.043 | 0.000 | 3.282 | 0.035 | 5.607 | 5.607 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 1.701 | 0.843 | [26] | 0.047 | 0.002 | 1.383 | 0.028 | 1.230 | 1.231 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.001 | 0.001 | [20] | 1.385 | 0.0 | 0.001 | 0.002 | 0.616 | 1.217 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.000 | [20] | 0.010 | 0.0 | 0.000 | 0.000 | 0.282 | 0.292 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.003 | 0.000 | [26] | 3.049 | 0.0 | 0.005 | 0.002 | 0.548 | 0.583 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.000 | [26] | 0.606 | 0.0 | 0.001 | 0.000 | 0.145 | 0.145 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.014073 | 0.000842 | NaN | 5.684483 | 0.000014 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.022532 | 0.000934 | 0.122191 | 0.624594 | 0.62513 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.341 | 0.005 | 0.235 | 0.0 | 0.349 | 0.007 | 0.976 | 0.976 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.539 | 0.091 | 0.520 | 0.0 | 0.509 | 0.242 | 3.025 | 3.351 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.014 | 0.001 | 5.684 | 0.0 | 0.023 | 0.001 | 0.625 | 0.625 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.000 | 0.826 | 0.0 | 0.000 | 0.000 | 0.520 | 0.559 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.000 | 4.419 | 0.0 | 0.000 | 0.000 | 0.482 | 0.674 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.000 | 0.009 | 0.0 | 0.000 | 0.000 | 0.629 | 0.663 | See | See |